Estimation of missing data by least squares

Dave Nichols

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ABSTRACT

I formulate the problem of estimating missing data using least squares. The operators that I use are error operators that have as output that part of the interpolated data that does not fit some parametric model of the data. If all the parameters of the model are known the problem is a linear least squares problem. If the parameters of the model must be estimated at the same time as the missing data the problem is non-linear. I use a model based on local linear events to interpolate aliased data. This procedure depends on good initial estimates of the dips which can be obtained from a smoothed version of the data.